Designing protein nanomaterials of predefined shape and characteristics has the potential to dramatically impact the medical industry. Machine learning (ML) has proven successful in protein design, reducing the need for expensive wet lab experiment rounds. However, challenges persist in efficiently exploring the protein fitness landscapes to identify optimal protein designs. In response, we propose the use of AlphaZero to generate protein backbones, meeting shape and structural scoring requirements. We extend an existing Monte Carlo tree search (MCTS) framework by incorporating a novel threshold-based reward and secondary objectives to improve design precision. This innovation considerably outperforms existing approaches, leading to protein backbones that better respect structural scores. The application of AlphaZero is novel in the context of protein backbone design and demonstrates promising performance. AlphaZero consistently surpasses baseline MCTS by more than 100% in top-down protein design tasks. Additionally, our application of AlphaZero with secondary objectives uncovers further promising outcomes, indicating the potential of model-based reinforcement learning (RL) in navigating the intricate and nuanced aspects of protein design

翻译：设计具有预定形状和特征的蛋白质纳米材料有可能极大地影响医疗行业。机器学习（ML）已被证明在蛋白质设计中取得成功，减少了对昂贵湿实验室实验轮次的需求。然而，在高效探索蛋白质适应度景观以识别最佳蛋白质设计方面，挑战依然存在。为此，我们提出使用AlphaZero生成蛋白质骨架，以满足形状和结构评分要求。我们扩展了现有的蒙特卡洛树搜索（MCTS）框架，通过引入新颖的基于阈值的奖励和次要目标来提高设计精度。这一创新显著优于现有方法，产生了更好地遵循结构评分的蛋白质骨架。AlphaZero在蛋白质骨架设计中的应用具有新颖性，并展示了有前景的性能。在自上而下的蛋白质设计任务中，AlphaZero始终优于基线MCTS超过100%。此外，我们使用具有次要目标的AlphaZero揭示了进一步的有前景结果，表明基于模型的强化学习（RL）在应对蛋白质设计中复杂而细微的方面具有潜力。